Method and system for evaluating power generation efficiency of heat collection field of photo-thermal power station

Abstract

The invention relates to the technical field of photo-thermal power generation, in particular to a photo-thermal power station heat collection field power generation efficiency evaluation method and system, which can provide more accurate and real-time power generation efficiency evaluation. The method comprises the steps that design parameters, historical operation data and historical heat collection efficiency of a heat collection field of the photo-thermal power station are collected; according to the design parameters of the heat collection field of the photo-thermal power station, a simulation model of the heat collection field is constructed to simulate the operation condition of the heat collection field of the photo-thermal power station, and the simulation model comprises a minute-level mechanism model and an artificial intelligence correction module; inputting the historical operation data into the simulation model to obtain historical predicted heat collection efficiency; performing deviation analysis on the historical predicted heat collection efficiency and the historical heat collection efficiency to obtain a model correction index; the model correction index is substituted into the simulation model, the model is corrected, and a heat collection efficiency evaluation model is obtained; and acquiring real-time operation data, and inputting the real-time operation data into the heat collection efficiency evaluation model to obtain the power generation efficiency of the heat collection field.

Description

Technical Field

[0001] The present invention relates to the technical field of solar thermal power generation, and in particular to a method and system for evaluating power generation efficiency of a solar thermal power station collector field. Background Art

[0002] With the increasing attention paid to renewable energy around the world, CSP has received extensive attention and development in recent years as a clean and efficient way of energy conversion. CSP stations use the heat energy generated by solar radiation to generate electricity through a series of conversion processes. In this process, the efficiency of the collector field directly determines the power generation capacity and economic benefits of the entire CSP station.

[0003] In the operation and management of CSP plants, accurate evaluation of the power generation efficiency of the collector field is crucial. However, since the operation of CSP plants is affected by many factors, the evaluation of the power generation efficiency of the collector field becomes complicated.

[0004] Traditional evaluation methods mainly rely on the statistics and analysis of historical operating data. Although this method can reflect the average performance of the collector field over the past period of time, it cannot accurately predict the power generation efficiency at future times, nor can it monitor and evaluate the real-time operating status of the collector field. In addition, due to the complex and changeable operating environment of the solar thermal power station, traditional statistical methods often find it difficult to capture the impact of these changes on the collector efficiency.

[0005] Therefore, there is an urgent need for a method that can comprehensively consider multiple influencing factors and accurately evaluate the power generation efficiency of the collector field of a solar thermal power station. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a method for evaluating the power generation efficiency of a solar thermal power station collector field, which can provide a more accurate and real-time evaluation of the power generation efficiency.

[0007] In a first aspect, the present invention provides a method for evaluating the power generation efficiency of a solar thermal power station collector field, the method comprising:

[0008] Collect design parameters, historical operation data and historical collection efficiency of the solar thermal power station collector field;

[0009] According to the design parameters of the solar thermal power station's collector field, a simulation model of the collector field is constructed to simulate the operation status of the solar thermal power station's collector field. The simulation model includes a minute-level mechanism model and an artificial intelligence correction module.

[0010] Input historical operation data into the simulation model to obtain historical predicted collector efficiency;

[0011] Perform deviation analysis on the historical predicted heat collection efficiency and the historical heat collection efficiency to obtain the model correction index;

[0012] Bring the model correction index into the simulation model, correct the model, and obtain the heat collection efficiency evaluation model;

[0013] Acquire real-time operating data and input the real-time operating data into the collector efficiency evaluation model to obtain the collector field power generation efficiency.

[0014] Furthermore, the calculation formula of the model correction index is:

[0015]

[0016] Where CI represents the correction index, n represents the number of data points in the deviation sequence, and y i represents the actual value of the historical collector efficiency, p i Represents the historical predicted collector efficiency.

[0017] Furthermore, the method for performing deviation analysis on the historical predicted heat collection efficiency and the historical heat collection efficiency includes:

[0018] Calculate the relative deviation between the predicted value and the actual value at each time point to obtain the deviation sequence;

[0019] Conduct statistical analysis on the deviation series, examine the deviation trend over time, and analyze the main causes of the deviation;

[0020] Based on the results of the deviation sequence analysis, the correction index is calculated.

[0021] Furthermore, the historical operation data includes solar radiation data, ambient temperature data, collector operation data, power generation system data and fault records.

[0022] Furthermore, the method for constructing a simulation model of a collector field includes:

[0023] Mathematical modeling of the physical processes in the collector field based on the principles of thermodynamics, optics and fluid mechanics;

[0024] Each physical process in the model is refined to a time scale of minutes to obtain a minute-level mechanism model;

[0025] Preprocess the historical operation data by cleaning and normalizing it for model training and verification;

[0026] Identify and extract factors that affect collector efficiency from historical operating data as input features;

[0027] Using a machine learning model as the infrastructure, taking some factors that affect the heat collection efficiency as input features, and learning the mapping relationship between the input features and the output feature heat collection efficiency;

[0028] Use another part of historical operation data that has not participated in the training to verify the trained model, and further optimize and adjust the model according to the verification results to obtain an artificial intelligence correction module;

[0029] The minute-level mechanism model and the artificial intelligence correction module are integrated to obtain a simulation model.

[0030] Furthermore, the model correction index is brought into the simulation model to correct the model:

[0031] For each parameter that needs to be corrected in the simulation model, use the corresponding correction index to adjust its value to obtain the adjusted new parameter;

[0032] The adjusted new parameters are calculated by the following formula:

[0033] x n =x i +CI i

[0034] Among them, x i Indicates the original parameters that need to be adjusted, CI i represents the correction index, x n Represents the new parameters after adjustment.

[0035] Furthermore, the method for obtaining the real-time power generation efficiency of the solar collector field includes:

[0036] Clean the collected real-time data to remove outliers and missing values;

[0037] Standardize different types of real-time data and unify data formats and units to facilitate model input;

[0038] Input the processed real-time operation data into the collector efficiency evaluation model;

[0039] After the data is input, the collector efficiency evaluation model performs calculations based on the simulation model, simulates the operating status of the collector field under current conditions, and outputs the real-time power generation efficiency of the collector field.

[0040] On the other hand, the present application also provides a system for evaluating the power generation efficiency of a solar thermal power station collector field, the system comprising:

[0041] Data collection module, which collects the design parameters, historical operation data and historical collection efficiency of the solar thermal power station collector field;

[0042] A simulation model building module, which builds a simulation model of the collector field according to the design parameters of the collector field of the CSP power station to simulate the operation status of the collector field of the CSP power station, wherein the simulation model includes a minute-level mechanism model and an artificial intelligence correction module;

[0043] The historical prediction module inputs the historical operation data into the simulation model to obtain the historical predicted heat collection efficiency;

[0044] Deviation analysis module, which performs deviation analysis on the historical predicted heat collection efficiency and the historical heat collection efficiency to obtain the model correction index;

[0045] The model correction module brings the model correction index into the simulation model, corrects the model, and obtains the heat collection efficiency evaluation model;

[0046] The real-time efficiency evaluation module obtains real-time operating data and inputs the real-time operating data into the collector efficiency evaluation model to obtain the real-time power generation efficiency of the collector field.

[0047] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0049] Compared with the prior art, the present invention has the following beneficial effects: the method not only takes into account the design parameters of the solar thermal power station collector field, but also combines historical operation data and historical collector efficiency, and can comprehensively reflect the operation status and performance of the collector field; by constructing a simulation model including a minute-level mechanism model and an artificial intelligence correction module, the method can comprehensively consider multiple influencing factors such as solar radiation intensity, ambient temperature, collector performance, etc., and improve the accuracy and reliability of the evaluation;

[0050] By comparing the simulation of the simulation model with historical data, it is possible to generate historical predicted thermal collection efficiencies and conduct deviation analysis with the actual historical thermal collection efficiencies, thereby continuously optimizing the model; this method can not only accurately evaluate the thermal collection efficiencies of the past and present, but also make high-precision predictions of power generation efficiency at future times;

[0051] The simulation model in this method includes an artificial intelligence correction module, which can dynamically correct the model according to real-time data and deviation analysis results; thus, the method can adapt to the complex and changeable operating environment of the CSP power station and improve the adaptability and accuracy of the assessment;

[0052] Accurately evaluating the power generation efficiency of the collector field helps CSP plants optimize their operation strategies, improve power generation capacity and economic benefits. By timely discovering and addressing potential operating problems, it can also avoid unnecessary downtime and economic losses, providing a strong guarantee for the long-term stable operation of CSP plants.

[0053] In summary, the above-mentioned CSP collector field power generation efficiency evaluation method can provide a more accurate and real-time power generation efficiency evaluation by combining simulation technology and artificial intelligence, thereby helping CSP stations achieve higher operating efficiency and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the present invention;

[0055] Figure 2 is a flow chart of a method for constructing a simulation model of a collector field;

[0056] Figure 3 The present invention is a structural diagram of a solar thermal power station collector field power generation efficiency evaluation system. DETAILED DESCRIPTION

[0057] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0058] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0059] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0060] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0061] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0062] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0063] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0064] The present application is described below in conjunction with the drawings in the present application.

[0065] Embodiment 1: Figure 1 to Figure 2 As shown, a method for evaluating the power generation efficiency of a solar thermal power station collector field of the present invention specifically comprises the following steps:

[0066] S1. Collect the design parameters, historical operation data and historical collection efficiency of the solar thermal power station collector field;

[0067] Design parameters are a series of key parameters determined during the construction and design of the solar thermal power station collector field. They have a direct impact on the performance of the collector field. The design parameters include:

[0068] Collector types and specifications: the different types of collectors used, as well as the size, material, and reflectivity of each collector;

[0069] Collector field layout: The arrangement, spacing, orientation and tilt angle of the collectors will affect the efficiency and distribution of the collectors in receiving solar radiation;

[0070] Heat transfer system parameters: including the type, flow rate, pressure and temperature range of the heat transfer medium;

[0071] Energy storage system parameters: including the type, capacity, and charging and discharging efficiency parameters of the energy storage medium;

[0072] Control system parameters: The structure and function of the control system, including sensors, actuators, controllers and communication protocols;

[0073] The historical operation data records the actual operation status of the collector field in the past period of time; the historical operation data includes:

[0074] Solar radiation data: including solar radiation intensity, sunshine duration and sun angle, reflecting the impact of solar radiation on the collector field;

[0075] Ambient temperature data: Changes in ambient temperature will affect the thermal efficiency and heat loss of the collector, so it is necessary to collect real-time monitoring data of ambient temperature;

[0076] Collector operation data: collector inlet and outlet temperatures, heat transfer medium flow and pressure, reflecting the actual operating conditions of the collector;

[0077] Power generation system data: including the output power, current, voltage and frequency of the generator set, reflecting the power generation capacity and stability of the CSP plant;

[0078] Fault records: Record faults, abnormal conditions and maintenance records that occur during the operation of the collector field, which helps to analyze the reliability and durability of the collector field;

[0079] The historical thermal collection efficiency is a direct indicator for evaluating the performance of the thermal collector field. It reflects the average thermal collection efficiency of the thermal collector field over a period of time in the past. The historical thermal collection efficiency is:

[0080] Thermal collection efficiency curve: The curve of thermal collection efficiency changing with solar radiation intensity, ambient temperature and other factors, reflecting the dynamic performance of the thermal collection field;

[0081] Average thermal efficiency: the average thermal efficiency of the thermal collector field over a certain period of time, used to evaluate the overall performance of the thermal collector field;

[0082] Efficiency loss analysis: Analyzing the causes and extent of collector efficiency loss helps optimize the design and operation strategy of the collector field.

[0083] In this step, by collecting design parameters, we can gain an in-depth understanding of the original intention, theoretical basis and technical specifications of the solar collector field during its construction and design; design parameters are the basis for evaluating the performance of the solar collector field, conducting fault diagnosis and optimizing the design; at the same time, historical operation data and historical collector efficiency provide performance data of the solar collector field in actual operation, which helps to comprehensively and objectively evaluate the performance of the solar collector field; historical operation data includes the actual operating conditions of the solar collector field over a period of time in the past. By analyzing these data, we can identify the performance of the solar collector field under different working conditions, so as to formulate more reasonable operation strategies and maintenance plans; this can not only improve the operating efficiency of the solar collector field, but also extend its service life; fault records include the collector Faults, abnormal conditions and maintenance records that occur during the operation of the thermal field; by analyzing these records, potential fault hazards can be discovered in time and corresponding preventive measures can be taken; this can not only reduce the occurrence of faults, but also reduce maintenance costs and maintenance difficulties; the data collected in this step provides necessary data support for subsequent steps; these data are the basis for building an accurate and reliable thermal collection efficiency evaluation model, and are also an important basis for optimizing the design and operation strategy of the thermal collection field; this step provides a solid foundation and strong support for subsequent thermal collection efficiency evaluation and optimization work by comprehensively collecting design parameters, historical operation data and historical thermal collection efficiency, which helps to improve the power generation capacity and economic benefits of the solar thermal power station.

[0084] S2. According to the design parameters of the solar thermal power station's solar collector field, a simulation model of the solar collector field is constructed to simulate the operation status of the solar thermal power station's solar collector field, wherein the simulation model includes a minute-level mechanism model and an artificial intelligence correction module;

[0085] Methods for constructing a simulation model of a collector field include:

[0086] Based on the principles of thermodynamics, optics and fluid mechanics, mathematical modeling is performed on the physical processes of solar radiation absorption, heat energy conversion and heat loss in the collector field; including the incident angle, reflection, absorption and transmission process of solar radiation, the process of heat energy conversion into the heat energy of the working medium through the collector, and the process of heat energy loss along the transmission pipeline;

[0087] Each physical process in the model is refined on a time scale of minutes to obtain a minute-level mechanism model; in order to achieve minute-level predictions, the model is able to handle changes in meteorological conditions such as solar radiation intensity, ambient temperature, and wind speed in a short period of time;

[0088] Preprocess the historical operation data by cleaning and normalizing it for model training and verification;

[0089] Identify and extract factors that affect collector efficiency from historical operating data as input features;

[0090] Using a machine learning model as the infrastructure, taking some factors that affect the heat collection efficiency as input features, and learning the mapping relationship between the input features and the output feature heat collection efficiency;

[0091] Use another part of historical operation data that has not participated in the training to verify the trained model and evaluate its prediction performance. According to the verification results, further optimize and adjust the model to obtain the artificial intelligence correction module.

[0092] Integrate the minute-level mechanism model and artificial intelligence correction module to obtain a complete simulation model.

[0093] In this step, through mathematical modeling based on the principles of thermodynamics, optics and fluid mechanics, as well as minute-level time scale refinement, the simulation model can accurately capture the dynamic changes of physical processes such as solar radiation absorption, thermal energy conversion and heat loss in the collector field; the model can accurately predict the power generation efficiency of the collector field under different meteorological conditions and operating conditions; because the model can handle changes in meteorological conditions in a short period of time and has strong real-time response capabilities, it helps the solar thermal power station to respond to changes in meteorological conditions in a timely manner, optimize operation strategies, and improve power generation efficiency and economic benefits; after the introduction of the artificial intelligence correction module, the model can use historical operation data to learn the mapping relationship between factors affecting the collector efficiency and the collector efficiency; it not only improves the prediction accuracy of the model, but also enables it to have self-learning and self-adaptive capabilities; by integrating the minute-level mechanism model and the artificial intelligence correction module, the simulation model can comprehensively evaluate the operating status of the collector field and discover potential problems and room for improvement; this provides strong support for the maintenance, upgrade and optimization of the solar thermal power station, and helps to improve the performance and reliability of the entire power station.

[0094] S3, inputting historical operation data into the simulation model to obtain historical predicted heat collection efficiency;

[0095] Methods for obtaining historical predicted collector efficiencies include:

[0096] Preprocess historical operation data, including data cleaning, data normalization and data partitioning;

[0097] The preprocessed historical operation data is used as input features and input into the simulation model. In the minute-level mechanism model, these input features will be used to simulate the physical processes such as solar radiation absorption, heat energy conversion and heat loss in the collector field. The artificial intelligence correction module will use these input features to learn the mapping relationship between the key factors affecting the collector efficiency and the collector efficiency.

[0098] After the model input is completed, the simulation model starts to run; the minute-level mechanism model will simulate the actual operation of the collector field based on the input meteorological conditions and operating parameters, and calculate the corresponding collector efficiency; the artificial intelligence correction module uses machine learning algorithms to correct the prediction results of the mechanism model to improve the accuracy of the prediction;

[0099] After the model is run and predicted, the historical predicted thermal collector efficiency value is obtained; the historical predicted thermal collector efficiency value reflects the predicted power generation efficiency of the thermal collector field at different historical moments.

[0100] In this step, by combining the minute-level mechanism model and the artificial intelligence correction module, this step can more accurately simulate the actual operating conditions of the solar collector field; the minute-level mechanism model can capture the dynamic changes of physical processes such as solar radiation absorption, thermal energy conversion and heat loss in the solar collector field, while the artificial intelligence correction module can use machine learning algorithms to correct the prediction results of the mechanism model, thereby improving the accuracy of the overall prediction; this step ensures the quality and consistency of the input data by preprocessing the historical operating data, including data cleaning, data normalization and data partitioning; it helps the simulation model to better adapt to different meteorological conditions and operating parameters, and improve the applicability and generalization ability of the model; this step obtains the historical predicted collector efficiency value by inputting the historical operating data into the simulation model and undergoing a series of processing and prediction processes, which provides an important basis for subsequent model correction and real-time evaluation and optimization.

[0101] S4. Perform deviation analysis on the historical predicted heat collection efficiency and the historical heat collection efficiency to obtain the model correction index;

[0102] The methods for analyzing the deviation between the historical predicted thermal collection efficiency and the historical thermal collection efficiency include:

[0103] Calculate the relative deviation between the predicted value and the actual value at each time point to obtain the deviation sequence;

[0104] Statistical analysis of the deviation sequence is performed to check the deviation change trend over time to see if there is a large deviation under specific conditions; the main causes of the deviation are analyzed, including the impact of changes in meteorological conditions, aging of collector performance, and adjustment of operating parameters on the prediction results; by comparing the deviations in different time periods and under different operating conditions, the key factors affecting the accuracy of the prediction can be identified;

[0105] According to the results of the deviation sequence analysis, the correction index is calculated; the model correction index is a quantitative indicator used to guide the subsequent correction of the simulation model. Through continuous iteration and optimization, the prediction deviation can be gradually reduced and the prediction accuracy of the model can be improved;

[0106] The calculation formula of the model correction index is:

[0107]

[0108] Where CI represents the correction index, n represents the number of data points in the deviation sequence, and y i represents the actual value of the historical collector efficiency, p i represents the historical predicted collector efficiency;

[0109] This formula calculates the average relative deviation between the predicted value and the actual value at all time points. The correction index CI reflects the overall degree of deviation of the predicted value relative to the actual value.

[0110] In this step, by performing deviation analysis on the historical predicted thermal collection efficiency and the historical thermal collection efficiency, the main sources of problems and deviations in the prediction model can be identified; this helps to carry out targeted corrections and optimizations on the simulation model in the future, thereby improving the prediction accuracy of the model; the calculated model correction index is a quantitative indicator that reflects the degree of deviation between the predicted value and the actual value; it provides a clear guiding direction for the correction of the model, making the correction process more scientific and systematic; by comparing and analyzing the deviations in different time periods and under different working conditions, the key factors affecting the prediction accuracy can be identified; it helps to enhance the adaptability of the model to different environments and working conditions, so that the model can maintain a high prediction accuracy in a wider range of application scenarios; the deviation analysis process is also a comprehensive review of the performance of the thermal collection system and the prediction technology; by continuously optimizing the prediction model, it can promote the continuous advancement of the thermal collection system design and prediction technology, and contribute to the development of the field of solar thermal utilization.

[0111] S5, bringing the model correction index into the simulation model, correcting the model, and obtaining a heat collection efficiency evaluation model;

[0112] The model correction index is introduced into the simulation model. The method of correcting the model includes:

[0113] For each parameter that needs to be corrected, use the corresponding correction index to adjust its value to obtain the adjusted new parameter;

[0114] The adjusted new parameters are calculated by the following formula:

[0115] x n =x i +CI i

[0116] Among them, x i Indicates the original parameters that need to be adjusted, CI i represents the correction index, x n Represents the new parameters after adjustment;

[0117] Use independent historical data sets to validate the revised model to ensure that the model can also show good predictive ability on unseen data;

[0118] Check the model's performance under different conditions to ensure that the model not only performs well on known data, but also maintains high prediction accuracy under new operating conditions.

[0119] In this step, by bringing the model correction index into the simulation model and making targeted adjustments to the model parameters, the deviation between the predicted value and the actual value can be significantly reduced, thereby improving the prediction accuracy of the model; using an independent historical data set to verify the corrected model can ensure that the model can also show good prediction capabilities on unseen data; it helps to enhance the generalization ability of the model so that it can adapt to more diverse operating conditions and environments; by checking the performance of the model under different conditions, it can ensure that the model not only performs well on known data, but also maintains high prediction accuracy under new operating conditions; it can improve the reliability of the model so that it can more accurately evaluate the power generation efficiency of the collector field and provide strong support for the operation and management of the solar thermal power station; with the improvement of the model accuracy, the solar thermal power station can more accurately predict the power generation efficiency of the collector field, thereby more reasonably allocating and scheduling resources; it helps to reduce operating costs, improve energy utilization efficiency, and maximize economic benefits; this step significantly improves the prediction accuracy, generalization ability and reliability of the model by introducing the model correction index to correct the simulation model, providing a strong guarantee for the accurate evaluation of the power generation efficiency of the collector field of the solar thermal power station.

[0120] S6. Acquire real-time operation data, and input the real-time operation data into a collector efficiency evaluation model to obtain the real-time power generation efficiency of the collector field;

[0121] Methods for obtaining the real-time power generation efficiency of the collector field include:

[0122] Clean the collected real-time data to remove outliers and missing values ​​to ensure data quality;

[0123] Standardize different types of real-time data and unify data formats and units to facilitate model input;

[0124] Input the processed real-time operation data into the collector efficiency evaluation model;

[0125] After the data is input, the collector efficiency evaluation model performs calculations based on the simulation model, simulates the operating status of the collector field under current conditions, and outputs the real-time power generation efficiency of the collector field;

[0126] The real-time power generation efficiency of the collector field calculated by the model reflects the performance of the collector field under current conditions;

[0127] Combine the calculated real-time power generation efficiency of the collector field with the real-time operation data to provide real-time operation status monitoring for operation managers;

[0128] Based on the real-time power generation efficiency of the collector field, operation and management personnel can make timely adjustments, optimize operation strategies, and improve power generation efficiency.

[0129] In this step, by acquiring and processing real-time operating data, this step can instantly reflect the current operating status of the solar collector field and accurately calculate the real-time power generation efficiency of the solar collector field, which helps to promptly discover and solve potential operating problems; before the data is input into the model, the collected real-time data is cleaned and processed to remove outliers and missing values, thereby ensuring the quality of the data; it can avoid misjudgment of the model due to inaccurate input data and improve the reliability of the evaluation results; different types of real-time data are standardized, the data format and unit are unified, the model input process is simplified, and the efficiency of data processing and model calculation is improved; rapid data input and immediate response of the model are achieved, which is a good basis for real-time evaluation It provides strong support; combines the calculated real-time power generation efficiency of the solar collector field with the real-time operation data to provide real-time operation status monitoring for the operation management personnel; helps the operation management personnel to fully understand the performance of the solar collector field, promptly discover performance degradation or abnormal operation, and make adjustments accordingly to optimize the operation strategy, thereby improving power generation efficiency; based on the evaluation results of the real-time power generation efficiency of the solar collector field, the operation management personnel can make more scientific and reasonable decisions; this step obtains and processes the operation data in real time, accurately evaluates the power generation efficiency of the solar collector field, provides real-time operation status monitoring and decision support for the operation management personnel, helps to optimize the operation strategy, improve power generation efficiency, and reduce operation costs.

[0130] Embodiment 2: Figure 3 As shown, a solar thermal power station collector field power generation efficiency evaluation system of the present invention specifically includes the following modules;

[0131] Data collection module, which collects the design parameters, historical operation data and historical collection efficiency of the solar thermal power station collector field;

[0132] A simulation model building module, which builds a simulation model of the collector field according to the design parameters of the collector field of the CSP power station to simulate the operation status of the collector field of the CSP power station, wherein the simulation model includes a minute-level mechanism model and an artificial intelligence correction module;

[0133] The historical prediction module inputs the historical operation data into the simulation model to obtain the historical predicted collector efficiency;

[0134] Deviation analysis module, which performs deviation analysis on the historical predicted heat collection efficiency and the historical heat collection efficiency to obtain the model correction index;

[0135] The model correction module brings the model correction index into the simulation model, corrects the model, and obtains the heat collection efficiency evaluation model;

[0136] The real-time efficiency evaluation module obtains real-time operating data and inputs the real-time operating data into the collector efficiency evaluation model to obtain the real-time power generation efficiency of the collector field.

[0137] The system obtains comprehensive design parameters, historical operating data and historical heat collection efficiency through the data collection module, providing a rich data foundation for the subsequent construction of simulation models; the simulation model not only considers the basic mechanism model, but also uses the artificial intelligence correction module to capture and simulate those complex and changeable influencing factors that are difficult to capture with traditional statistical methods, thereby improving the accuracy of the evaluation;

[0138] Different from the traditional statistical method that relies on historical data, this system can realize real-time evaluation of the power generation efficiency of the collector field by building a simulation model and inputting real-time data. The real-time efficiency evaluation module can instantly reflect the operating status of the collector field, providing timely information support for the operation and management of the solar thermal power station. Through model correction and comparison of historical data, the system can also predict the power generation efficiency at a certain degree in the future.

[0139] The minute-level mechanism model in the simulation model can accurately simulate the operating conditions of the collector field, improving the accuracy of the assessment; the artificial intelligence correction module can dynamically correct the model according to the deviation analysis results, making the model closer to the actual operating conditions and enhancing the flexibility and adaptability of the system;

[0140] By accurately evaluating the power generation efficiency of the collector field, the system can help CSP plants optimize their operation strategies, improve power generation capacity and economic benefits; the real-time evaluation function can also promptly detect and handle potential operation problems, avoiding unnecessary downtime and economic losses;

[0141] In summary, the collector field power generation efficiency evaluation system of the CSP power station can effectively solve the shortcomings of traditional evaluation methods and provide more accurate and real-time power generation efficiency evaluation, thereby helping CSP power stations achieve higher operating efficiency and economic benefits.

[0142] The various variations and specific embodiments of the method for evaluating the power generation efficiency of a solar thermal power station collector field in the aforementioned embodiment 1 are also applicable to a solar thermal power station collector field power generation efficiency evaluation system in this embodiment. Through the aforementioned detailed description of the method for evaluating the power generation efficiency of a solar thermal power station collector field, those skilled in the art can clearly understand the implementation method of the solar thermal power station collector field power generation efficiency evaluation system in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0143] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0144] The above is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the technical principle of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for evaluating the power generation efficiency of a solar thermal power station collector field, characterized in that: The method comprises: Collect design parameters, historical operation data and historical collection efficiency of the solar thermal power station collector field; According to the design parameters of the solar thermal power station's solar collector field, a simulation model of the solar collector field is constructed to simulate the operation status of the solar thermal power station's solar collector field, wherein the simulation model includes a minute-level mechanism model and an artificial intelligence correction module; Input historical operation data into the simulation model to obtain historical predicted collector efficiency; Deviation analysis is performed on the historical predicted heat collection efficiency and the historical heat collection efficiency to obtain the model correction index; Bring the model correction index into the simulation model, correct the model, and obtain the heat collection efficiency evaluation model; Acquire real-time operating data and input the real-time operating data into the collector efficiency evaluation model to obtain the real-time power generation efficiency of the collector field.

2. A method for evaluating the power generation efficiency of a solar thermal power station collector field according to claim 1, characterized in that: The calculation formula of the model correction index is: Where CI represents the correction index, n represents the number of data points in the deviation sequence, and y i represents the actual value of the historical collector efficiency, p i Represents the historical predicted collector efficiency.

3. A method for evaluating the power generation efficiency of a solar thermal power station collector field according to claim 2, characterized in that: The methods for analyzing the deviation between the historical predicted thermal collection efficiency and the historical thermal collection efficiency include: Calculate the relative deviation between the predicted value and the actual value at each time point to obtain the deviation sequence; Conduct statistical analysis on the deviation series, examine the deviation trend over time, and analyze the main causes of the deviation; Based on the results of the deviation sequence analysis, the correction index is calculated.

4. A method for evaluating the power generation efficiency of a solar thermal power station collector field according to claim 1, characterized in that: The historical operation data includes solar radiation data, ambient temperature data, collector operation data, power generation system data and fault records.

5. A method for evaluating the power generation efficiency of a solar thermal power station collector field according to claim 1, characterized in that: Methods for constructing a simulation model of a collector field include: Mathematical modeling of the physical processes in the collector field based on the principles of thermodynamics, optics and fluid mechanics; Each physical process in the model is refined to a time scale of minutes to obtain a minute-level mechanism model; Preprocess the historical operation data by cleaning and normalizing it for model training and verification; Identify and extract factors that affect collector efficiency from historical operating data as input features; Using a machine learning model as the infrastructure, taking some factors that affect the heat collection efficiency as input features, and learning the mapping relationship between the input features and the output feature heat collection efficiency; Use another part of historical operation data that has not participated in the training to verify the trained model, and further optimize and adjust the model according to the verification results to obtain an artificial intelligence correction module; The minute-level mechanism model and the artificial intelligence correction module are integrated to obtain a simulation model.

6. A method for evaluating the power generation efficiency of a solar thermal power station collector field according to claim 1, characterized in that: Bring the model correction index into the simulation model to correct the model: For each parameter that needs to be corrected in the simulation model, use the corresponding correction index to adjust its value to obtain the adjusted new parameter; The adjusted new parameters are calculated by the following formula: x n =x i +CI i Among them, x i Indicates the original parameters that need to be adjusted, CI i represents the correction index, x n Represents the new parameters after adjustment.

7. A method for evaluating the power generation efficiency of a solar thermal power station collector field according to claim 1, characterized in that: Methods for obtaining the real-time power generation efficiency of the collector field include: Clean the collected real-time data to remove outliers and missing values; Standardize different types of real-time data and unify data formats and units to facilitate model input; Input the processed real-time operation data into the collector efficiency evaluation model; After the data is input, the collector efficiency evaluation model performs calculations based on the simulation model, simulates the operating status of the collector field under current conditions, and outputs the real-time power generation efficiency of the collector field.

8. A system for evaluating the power generation efficiency of a solar thermal power station collector field, characterized in that: The system comprises: Data collection module, which collects the design parameters, historical operation data and historical collection efficiency of the solar thermal power station collector field; A simulation model building module, which builds a simulation model of the collector field according to the design parameters of the collector field of the CSP power station to simulate the operation status of the collector field of the CSP power station, wherein the simulation model includes a minute-level mechanism model and an artificial intelligence correction module; The historical prediction module inputs the historical operation data into the simulation model to obtain the historical predicted collector efficiency; Deviation analysis module, which performs deviation analysis on the historical predicted heat collection efficiency and the historical heat collection efficiency to obtain the model correction index; The model correction module brings the model correction index into the simulation model, corrects the model, and obtains the heat collection efficiency evaluation model; The real-time efficiency evaluation module obtains real-time operating data and inputs the real-time operating data into the collector efficiency evaluation model to obtain the real-time power generation efficiency of the collector field.

9. An electronic device for measuring the power generation efficiency of a solar thermal power station collector field, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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